The Dark Energy Survey : The Dark Energy Survey Josh Frieman
White Papers submitted to
Dark Energy Task Force:
astro-ph/0510346
Theoretical & Computational
Challenges:
astro-ph/0510194,5
The Dark Energy Survey : The Dark Energy Survey Study Dark Energy using
4 complementary* techniques:
I. Cluster Counts
II. Weak Lensing
III. Baryon Acoustic Oscillations
IV. Supernovae
• Two multiband surveys:
5000 deg2 g, r, i, z
40 deg2 repeat (SNe)
• Build new 3 deg2 camera
and Data management sytem
Survey 2009-2015 (525 nights)
Response to NOAO AO
Blanco 4-meter at CTIO *in systematics & in cosmological parameter degeneracies
*geometric+structure growth: test Dark Energy vs. Gravity
The DES Collaboration : The DES Collaboration Fermilab: J. Annis, H. T. Diehl, S. Dodelson, J. Estrada, B. Flaugher, J. Frieman, S. Kent, H. Lin, P. Limon, K. W. Merritt, J. Peoples, V. Scarpine, A. Stebbins,
C. Stoughton, D. Tucker, W. Wester
University of Illinois at Urbana-Champaign: C. Beldica, R. Brunner, I. Karliner, J. Mohr, R. Plante, P. Ricker, M. Selen, J. Thaler
University of Chicago: J. Carlstrom, S. Dodelson, J. Frieman, M. Gladders,
W. Hu, S. Kent, R. Kessler, E. Sheldon, R. Wechsler
Lawrence Berkeley National Lab: N. Roe, C. Bebek, M. Levi, S. Perlmutter
University of Michigan: R. Bernstein, B. Bigelow, M. Campbell, D. Gerdes, A. Evrard, W. Lorenzon, T. McKay, M. Schubnell, G. Tarle, M. Tecchio
NOAO/CTIO: T. Abbott, C. Miller, C. Smith, N. Suntzeff, A. Walker
CSIC/Institut d'Estudis Espacials de Catalunya (Barcelona): F. Castander, P. Fosalba, E. Gaztañaga, J. Miralda-Escude
Institut de Fisica d'Altes Energies (Barcelona): E. Fernández, M. Martínez
CIEMAT (Madrid): C. Mana, M. Molla, E. Sanchez, J. Garcia-Bellido
University College London: O. Lahav, D. Brooks, P. Doel, M. Barlow, S. Bridle, S. Viti, J. Weller
University of Cambridge: G. Efstathiou, R. McMahon, W. Sutherland
University of Edinburgh: J. Peacock
University of Portsmouth: R. Crittenden, R. Nichol, W. Percival
University of Sussex: A. Liddle, K. Romer plus students
Photometric Redshifts : Photometric Redshifts • Measure relative flux in
four filters griz:
track the 4000 A break
• Estimate individual galaxy
redshifts with accuracy
(z) < 0.1 (~0.02 for clusters)
• Precision is sufficient
for Dark Energy probes,
provided error distributions
well measured.
• Note: good detector response in
z band filter needed to reach z>1
Elliptical galaxy spectrum
Slide5 : DES
griz filters 10 Limiting Magnitudes
g 24.6
r 24.1
i 24.0
z 23.9
+2% photometric calibration
error added in quadrature
Key: Photo-z systematic errors
under control using existing
spectroscopic training sets to
DES photometric depth
Galaxy Photo-z Simulations +VDES JK Improved Photo-z & Error Estimates and robust methods of outlier rejection DES Cunha, etal DES + VDES on
ESO VISTA 4-m
enhances science reach
I. Clusters and Dark Energy : I. Clusters and Dark Energy
Mohr Volume Growth
(geometry) Number of clusters above observable mass threshold Dark Energy
equation of state Requirements
Understand formation of dark matter halos
Cleanly select massive dark matter halos (galaxy clusters) over a range of redshifts
Redshift estimates for each cluster
Observable proxy that can be used as cluster mass estimate:
O =g(M)
Primary systematic:
Uncertainty in bias & scatter of mass-observable relation
Cluster Cosmology with DES : Cluster Cosmology with DES 3 Techniques for Cluster Selection and Mass Estimation:
Optical galaxy concentration
Weak Lensing
Sunyaev-Zel’dovich effect (SZE)
Cross-compare these techniques to reduce systematic errors
Additional cross-checks:
shape of mass function; cluster
correlations
10-m South Pole Telescope (SPT) : 10-m South Pole Telescope (SPT) SPT will carry out 4000 sq. deg. SZE
Survey PI: J. Carlstrom (U. Chicago) NSF-OPP funded & scheduled for Nov 2006 deployment
DOE (LBNL) funding of readout development Sunyaev-Zel’dovich effect
Compton upscattering of CMB photons
by hot gas in clusters
- nearly independent of redshift:
- can probe to high redshift
- need ancillary redshift measurement Dec 2005
SZE vs. Cluster Mass: Progress in Realistic Simulations : SZE vs. Cluster Mass: Progress in Realistic Simulations Motl, etal Integrated SZE flux decrement depends only on cluster mass: insensitive to details of gas dynamics/galaxy formation in the cluster core robust scaling relations Nagai SZE flux Adiabatic
∆ Cooling+Star
Formation SPT Observable Kravtsov
Future:
SCIDAC
proposal small (~10%)
scatter
Slide10 : Statistical Weak Lensing Calibrates
Cluster Mass vs. Observable Relation Cluster Mass
vs. Number
of galaxies they
contain
For DES, will
use this to
independently
calibrate
SZE vs. Mass Johnston, Sheldon, etal, in preparation
Statistical
Lensing
eliminates
projection effects
of individual
cluster mass
estimates
Johnston, etal
astro-ph/0507467 SDSS Data
Preliminary
z<0.3
Slide11 : Observer Dark matter halos Background sources Statistical measure of shear pattern, ~1% distortion
Radial distances depend on geometry of Universe
Foreground mass distribution depends on growth of structure
Slide12 : Cosmic Shear
Angular Power
Spectrum in 4
Photo-z Slices
Shapes of ~300
million galaxies
median redshift z = 0.7
Primary Systematics:
photo-z’s, PSF anisotropy,
shear calibration Weak Lensing Tomography DES WL forecasts conservatively assume 0.9” PSF = median delivered to
existing Blanco camera: DES should do better & be more stable (see Brenna’s talk) Huterer Statistical errors
shown
Reducing WL Shear Systematics : Reducing WL Shear Systematics See Brenna’s talk
for DECam+Blanco
hardware
improvements that
will reduce raw
lensing systematics Red: expected signal Results from
75 sq. deg. WL
Survey with
Mosaic II and BTC
on the Blanco 4-m
Bernstein, etal
DES: comparable
depth: source
galaxies well
resolved & bright:
low-risk (improved systematic) (signal) Shear systematics under control at level needed for DES (old systematic) Cosmic Shear
III. Baryon Acoustic Oscillations (BAO) in the CMB : III. Baryon Acoustic Oscillations (BAO) in the CMB Characteristic angular scale set by sound horizon at recombination: standard ruler (geometric probe).
Baryon Acoustic Oscillations: CMB & Galaxies : Baryon Acoustic Oscillations: CMB & Galaxies CMB
Angular
Power
Spectrum SDSS galaxy
correlation function Acoustic series in P(k) becomes a single peak in (r) Bennett, etal Eisenstein etal
Slide16 : BAO in DES: Galaxy Angular Power Spectrum Probe substantially larger volume and redshift range than SDSS Wiggles due
to BAO Blake & Bridle Fosalba & Gaztanaga
IV. Supernovae : IV. Supernovae Geometric Probe of Dark Energy
Repeat observations of 40 deg2 , using 10% of survey time
• ~1900 well-measured SN Ia
lightcurves, 0.25 < z < 0.75
Larger sample, improved z-band response compared to ESSENCE, SNLS; address issues they raise
Improved photometric precision via in-situ photometric response measurements SDSS
DES Forecasts: Power of Multiple Techniques : DES Forecasts: Power of Multiple Techniques Ma, Weller,
Huterer, etal Assumptions:
Clusters:
8=0.75, zmax=1.5,
WL mass calibration
(no clustering)
BAO: lmax=300
WL: lmax=1000
(no bispectrum)
Statistical+photo-z
systematic errors only
Spatial curvature, galaxy bias
marginalized
Planck CMB prior w(z) =w0+wa(1–a) 68% CL geometric geometric+
growth Clusters
if 8=0.9
Slide19 : Will measure Dark Energy using multiple complementary probes, developing these techniques and exploring their systematic error floors
Survey strategy delivers substantial DE science after 2 years
Relatively modest, low-risk, near-term project with high discovery potential
Scientific and technical precursor to the more ambitious Stage IV Dark Energy projects to follow: LSST and JDEM
DES in unique international position to synergize with SPT and VISTA on the DETF Stage III timescale (PanSTARRS is in the Northern hemisphere; cannot be done with existing facilities in the South) DES and a Dark Energy Program
Extra Slides : Extra Slides
Slide21 : Spectroscopic Redshift
Training Sets for DES Training Sets to the DES photometric depth in place
(advantage of a `relatively’ shallow survey)
Slide22 : DES Cluster Photometric Redshift Simulations DES:
for clusters,
(z) < 0.02 for z <1.3
DES+VDES
griz+JK on VISTA:
extend photo-z’s to
z~2
(enhances, but not
critical to, science
goals)
Bias : Variance and Bias of Photo-z Estimates Cunha etal Variance Bias
Photo-z Error Distributions & Error Estimates : Photo-z Error Distributions & Error Estimates
Robustly Reducing Catastrophic Errors : Robustly Reducing Catastrophic Errors Remove 10% of objects via color cuts 30% improvement Original 10% Cut
Clusters and Photo-z Systematics : Clusters and Photo-z Systematics
Weak Lensing & Photo-z Systematics : Weak Lensing & Photo-z Systematics Ma (w0)/(w0|pz fixed) (wa)/(wa|pz fixed)
BAO & Photo-z Systematics : BAO & Photo-z Systematics Ma (w0)/(w0|pz fixed) (wa)/(wa|pz fixed)
Supernovae and photo-z errors : Supernovae and photo-z errors Huterer
Improving Corrections for Anisotropic PSF : Improving Corrections for Anisotropic PSF Whisker plots for three BTC camera exposures; ~10% ellipticity
Left and right are most extreme variations, middle is more typical.
Correlated variation in the different exposures: PCA analysis -->
can use stars in all the images: much better PSF interpolation Focus too low Focus (roughly) correct Focus too high Jarvis and Jain
PCA Analysis : PCA Analysis Remaining ellipticities are essentially uncorrelated.
Measurement error is the cause of the residual shapes.
1st improvement: higher order polynomial means PSF accurate to smaller scales
2nd: Much lower correlated residuals on all scales Focus too low Focus (roughly) correct Focus too high
Slide32 : Image Lensing Cluster Source Tangential shear
Slide33 : Statistical Weak Lensing by Galaxy Clusters Mean
Tangential
Shear
Profile
in Optical
Richness
(Ngal) Bins
to 30 h-1Mpc
Sheldon,
Johnston, etal
SDSS preliminary
Slide34 : Johnston, Sheldon, etal
SDSS preliminary Invert Mean Shear Profile to obtain Mean Mass Profile Virial Mass Virial radius
Precision Cosmology with Clusters : Precision Cosmology with Clusters Requirements
Understand formation of dark matter halos
Cleanly select massive dark matter halos (galaxy clusters) over a range of redshifts
Redshift estimates for each cluster
Observable proxy that can be used as cluster mass estimate:
O =g(M)
Primary systematic:
Uncertainty in bias & scatter of mass-observable relation
Mass
threshold
Forecasts for Constant w Models : Forecasts for Constant w Models (DE) (w)
Forecasts with WMAP Priors : Forecasts with WMAP Priors (w0) (wa)